用解剖运动模式提升心超左室线性测量精度
EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode
- 基于解剖M型图像训练地标检测器，强制直线约束提高准确性
- 相比传统方法，测量误差显著降低，跨模型泛化能力强
- 半自动设计只需用户划线，临床交互更简便
通过B模式超声心动图在心尖长轴视图中对左心室（LV）进行线性测量，对心脏评估至关重要。该过程需在靠近二尖瓣尖端的虚拟扫描线（SL）上标记4至6个解剖点，该扫描线垂直于左心室轴线。人工标记耗时且易出错，现有深度学习方法常出现地标错位，导致测量不准确。本文提出一种新框架，通过施加直线约束来增强左心室测量精度。首先在实时计算的解剖M型（AMM）图像上训练地标检测器，再将其结果映射回B模式空间。该方法有效缓解了地标错位问题，减少测量误差。实验表明，本方法在准确性上优于标准B模式方法，且在不同网络架构间具有良好泛化能力。采用半自动设计，用户仅需划定扫描线，简化操作流程的同时保持对齐灵活性与临床适用性。
原文摘要 · Abstract (English)
Linear measurements of the left ventricle (LV) in the Parasternal Long Axis (PLAX) view using B-mode echocardiography are crucial for cardiac assessment. These involve placing 4-6 landmarks along a virtual scanline (SL) perpendicular to the LV axis near the mitral valve tips. Manual placement is time-consuming and error-prone, while existing deep learning methods often misalign landmarks, causing inaccurate measurements. We propose a novel framework that enhances LV measurement accuracy by enforcing straight-line constraints. A landmark detector is trained on Anatomical M-Mode (AMM) images, computed in real time from B-mode videos, then transformed back to B-mode space. This approach addresses misalignment and reduces measurement errors. Experiments show improved accuracy over standard B-mode methods, and the framework generalizes well across network architectures. Our semi-automatic design includes a human-in-the-loop step where the user only places the SL, simplifying interaction while preserving alignment flexibility and clinical relevance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。